11 citations · 15 across the 6 of their papers we have counts for
6 papers
LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation
Ke Guo, Zhenwei Miao, Wei Jing +4
Microscopic traffic simulation plays a crucial role in transportation engineering by providing insights into individual vehicle behavior and overall traffic flow. However, creating…
Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning
Liu Weiwei, Hu Wenxuan, Jing Wei +3
Autonomous vehicles trained through Multi-Agent Reinforcement Learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained pol…
FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving
Tengju Ye, Wei Jing, Chunyong Hu +11
Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving. However, leveraging such…
Long-term Microscopic Traffic Simulation with History-Masked Multi-agent Imitation Learning
Ke Guo, Wei Jing, Lingping Gao +3
A realistic long-term microscopic traffic simulator is necessary for understanding how microscopic changes affect traffic patterns at a larger scale. Traditional simulators that mo…
CCIL: Context-conditioned imitation learning for urban driving
Ke Guo, Wei Jing, Junbo Chen +1
Imitation learning holds great promise for addressing the complex task of autonomous urban driving, as experienced human drivers can navigate highly challenging scenarios with ease…
Zero-shot Transfer Learning of Driving Policy via Socially Adversarial Traffic Flow
Dongkun Zhang, Jintao Xue, Yuxiang Cui +6
Acquiring driving policies that can transfer to unseen environments is challenging when driving in dense traffic flows. The design of traffic flow is essential and previous studies…